🆕What is LLMOps?

LLMOps represents a groundbreaking discipline specifically developed to handle the unique requirements and challenges posed by large language models.

Delving into the specifics, LLMs refer to deep learning models with the unique ability to generate human language-like outputs, hence they are designated as 'language models.' These models are characterized by their size, possessing billions of parameters and having been trained on billions of words, thereby earning the title 'large language models.'

On the other hand, MLOps is a set of established best practices and tools dedicated to the efficient management of the lifecycle of applications driven by machine learning.

With these definitions in mind, we can now understand that LLMOps is essentially the application of MLOps principles and tools to the unique challenges and needs of applications powered by LLMs.

Broadly speaking, LLMOps landscape today has:

• Platforms where you can fine-tune, version and deploy LLMs while these platforms handle the infrastructure behind the scenes.

• No-code and low-code platforms built specifically for LLMs where the abstraction layer is set very high to make it easy to adopt, but the flexibility will be limited.

• Code-first platforms (incl. certain MLOps platforms) built more broadly for custom ML systems that may include LLMs and other foundational models. A combination of high flexibility and easy access to compute for expert users.

• Frameworks which make it easier to develop LLM applications (for example, standardizing interfaces between different LLMs and dealing with prompts).

• Ancillary tools built to streamline a smaller part of the workflow, such as testing prompts, incorporating human feedback (RLHF) or evaluating datasets.

LLMOps Ecosystem: https://wagmivs.notion.site/4796d41676734397a1aeee6efd5691e3?v=0e01d01c0ee043f183fe75eab72fb7b5&pvs=4